Hands-on Exercise 02-1: First-order Spatial Point Pattern Analysis

Overview

Spatial Point Pattern Analysis (SPPA) refers to the study of how points are arranged or distributed across a given surface. There points may represent:

• Events, such as crimes, road accidents, or disease occurrences, or

• Service and facility locations, including shops (e.g., cafes, supermarkets) and community facilities like childcare or aged care centres.

First-order Spatial Point Pattern Analysis (1-st SPPA) examines the intensity or density of points across a study area. It identifies spatial trends in point distribution without considering interaction between points helping to answer questions like:

• Where are points most concentrated?

• Is density uniform or variable?

• How dispersed is the pattern?

In this exercise, the spatsat is utilised to apply two common 1st-SPPA methods to explore:

1. Whether childcare centres in Singapore are randomly distributed;

2. If not, identifying areas with higher concentrations of centres.

The Data

The following datasets were downloaded from publicly available websites, and both are available in KML and GeoJSON format.

Dataset Name	Source	Discrption
Child Care Services	data.gov.sg	Point feature data: contains the locations and attributes of childcare centres.
Master plan 2019 Subzone Boundary (No Sea)	singstat	Polygon feature data: represents the URA 2019 Master Plan planning subzone boundaries.

Installing and Loading the R Packages

In addition to spatstat, a total of five R packages will be used in this exercise.

Package	Discription
sf	Simple Features, a new R package which handles importing, managing, and processing vector-based geospatial data.
spatstat	Provides useful functions for SPPA, which will be called to conduct both 1st and 2nd SPPA and KDE.
terra	Modern package for raster/vector spatial data and will be used to convert spastat outputs into terra format.
tmap	Creates high quality static or interactive choropleth maps via leaflet.
rvest	Scrapes and extracts data from web pages.

After installation, we load them into R environment using the code below.

pacman:: p_load(sf, terra, spatstat, tmap, rvest, tidyverse)

Importing and Wrangling Geospatial Data Sets

The following code chunk shows the steps to first import the Master plan 2019 Subzone Boundary (No Sea) data using st_read, extract the required 4 columns from the Description field, filter out the nearby islands, and finally save the file as mpsz_cl for further analysis.

mpsz_sf <- st_read("data/MasterPlan2019SubzoneBoundaryNoSeaKML.kml") %>%
  st_zm(drop = TRUE, what = "ZM") %>%
  st_transform(crs = 3414)

Reading layer `URA_MP19_SUBZONE_NO_SEA_PL' from data source 
  `C:\lsrgc\ISSS626-yiqiong-pan\Hands-on_Ex\Hands-on_Ex02_1\data\MasterPlan2019SubzoneBoundaryNoSeaKML.kml' 
  using driver `KML'
Simple feature collection with 332 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS:  WGS 84

extract_kml_field <- function(html_text, field_name) {
  if (is.na(html_text) || html_text == "") return(NA_character_)
  
  page <- read_html(html_text)
  rows <- page %>% html_elements("tr")
  
  value <- rows %>%
    keep(~ html_text2(html_element(.x, "th")) == field_name) %>%
    html_element("td") %>%
    html_text2()
  
  if (length(value) == 0) NA_character_ else value
}

# map_chr of purr (tidyverse) applies a function to each element of a list/vector and returns a character vector.
mpsz_sf <- mpsz_sf %>%
  mutate(
    REGION_N = map_chr(Description, extract_kml_field, "REGION_N"),
    PLN_AREA_N = map_chr(Description, extract_kml_field, "PLN_AREA_N"),
    SUBZONE_N = map_chr(Description, extract_kml_field, "SUBZONE_N"),
    SUBZONE_C = map_chr(Description, extract_kml_field, "SUBZONE_C")
  ) %>%
  select(-Name, -Description) %>%
  relocate(geometry, .after = last_col())

mpsz_cl <- mpsz_sf %>%
  filter(SUBZONE_N != "SOUTHERN GROUP",
         PLN_AREA_N != "WESTERN ISLANDS",
         PLN_AREA_N != "NORTH-EASTERN ISLANDS")

write_rds(mpsz_cl,
          "data/mpsz_cl.rds")

The code chuck below imports downloaded ChildCareServices data to R as sf data frame as childcare_sf by using st_read, coverts 3d to 2d (st_zm) and finally transform the CRS from WGS84 to SVY21.

childcare_sf <- st_read("data/ChildCareServices.geojson") %>%
  st_zm(drop = TRUE, what = "ZM") %>% # Drop Z and M to convert from multi-dimensional to 2d (XY)
  st_transform(crs = 3414)

Reading layer `ChildCareServices' from data source 
  `C:\lsrgc\ISSS626-yiqiong-pan\Hands-on_Ex\Hands-on_Ex02_1\data\ChildCareServices.geojson' 
  using driver `GeoJSON'
Simple feature collection with 1925 features and 2 fields
Geometry type: POINT
Dimension:     XYZ
Bounding box:  xmin: 103.6878 ymin: 1.247759 xmax: 103.9897 ymax: 1.462134
z_range:       zmin: 0 zmax: 0
Geodetic CRS:  WGS 84

Mapping the Geospatial Data Sets

Using the tmap mapping methods, the code chunk below creates a map combining childcare_sf and mpsz_cl.

tm_shape(mpsz_cl)+
  tm_fill(col = "grey" ) +
  tm_borders(col = "black") +
tm_shape(childcare_sf) +
  tm_dots(col = "black")

Alternatively, an interactive thematic map can be plotted using the code below. The interactive map is easy to navigate and query intuitively. It is optional to change the background map layer(choices: ESRI.WorldGrayCanvas(default), OpenStreetMap, ESRI.WorldTopoMap).

tmap_mode('view')
tm_shape(childcare_sf) +
  tm_dots() #creates a layer of dots to visualise point data on a map.

tmap_mode('plot') #switch back static maps

Warning

It is advised to always switch back to plot mode to save connection consumption and limit the number of interactive maps to 10 in one documents when publishing.

Geospatial Data Wrangling

In this section, the data (sf objects) will be converted to spatstat data structure: ppp (for point data) and owin (for polygonal data).

Converting sf Data Frames to PPP

Here we use as.ppp() of spatstat package to covert the point data childcare_sf to ppp file, confirm the change using class() and have a quick overview of the data statistics via summary().

childcare_ppp <- as.ppp(childcare_sf)
class(childcare_ppp)

[1] "ppp"

summary(childcare_ppp)

Marked planar point pattern:  1925 points
Average intensity 2.417323e-06 points per square unit

Coordinates are given to 11 decimal places

Mark variables: Name, Description
Summary:
     Name           Description       
 Length:1925        Length:1925       
 Class :character   Class :character  
 Mode  :character   Mode  :character  

Window: rectangle = [11810.03, 45404.24] x [25596.33, 49300.88] units
                    (33590 x 23700 units)
Window area = 796335000 square units

Creating Owin Object

Similarly, the owin object can be created using the function as.owin() for polygon data. After the conversion, the class() and plot() functions can be used to verify that the object is of the correct class and that the data retains its original shape.

sg_owin <- as.owin(mpsz_cl)
class(sg_owin)

[1] "owin"

plot(sg_owin)

Combining Point Events object and Owin Object

childcareSG_PPP = childcare_ppp[sg_owin]
class(childcareSG_PPP)

[1] "ppp"

plot(childcareSG_PPP)